Pandas Profiling Report

Dataset statistics

Number of variables	13
Number of observations	6717
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	682.3 KiB
Average record size in memory	104.0 B

Variable types

Numeric	8
Categorical	5

Alerts

`name` has a high cardinality: 1982 distinct values	High cardinality
`year` is highly correlated with `selling_price` and 1 other fields	High correlation
`selling_price` is highly correlated with `year` and 1 other fields	High correlation
`km_driven` is highly correlated with `year`	High correlation
`seats` is highly correlated with `engine CC`	High correlation
`engine CC` is highly correlated with `seats` and 1 other fields	High correlation
`power BHP` is highly correlated with `selling_price` and 1 other fields	High correlation
`selling_price` is highly correlated with `power BHP`	High correlation
`seats` is highly correlated with `engine CC`	High correlation
`mileage KMPL` is highly correlated with `engine CC`	High correlation
`engine CC` is highly correlated with `seats` and 2 other fields	High correlation
`power BHP` is highly correlated with `selling_price` and 1 other fields	High correlation
`year` is highly correlated with `selling_price`	High correlation
`selling_price` is highly correlated with `year`	High correlation
`engine CC` is highly correlated with `power BHP`	High correlation
`power BHP` is highly correlated with `engine CC`	High correlation
`year` is highly correlated with `owner`	High correlation
`selling_price` is highly correlated with `owner` and 1 other fields	High correlation
`fuel` is highly correlated with `engine CC`	High correlation
`transmission` is highly correlated with `power BHP`	High correlation
`owner` is highly correlated with `year` and 1 other fields	High correlation
`seats` is highly correlated with `mileage KMPL` and 1 other fields	High correlation
`mileage KMPL` is highly correlated with `seats` and 1 other fields	High correlation
`engine CC` is highly correlated with `fuel` and 3 other fields	High correlation
`power BHP` is highly correlated with `selling_price` and 2 other fields	High correlation
`df_index` has unique values	Unique

Reproduction

Analysis started	2022-04-29 02:11:15.641669
Analysis finished	2022-04-29 02:11:24.449884
Duration	8.81 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

df_index
Real number (ℝ_≥0)

UNIQUE

Distinct	6717
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3957.142177

Minimum	0
Maximum	8125
Zeros	1
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	52.6 KiB

Quantile statistics

Minimum	0
5-th percentile	350.8
Q1	1915
median	3869
Q3	5992
95-th percentile	7669.2
Maximum	8125
Range	8125
Interquartile range (IQR)	4077

Descriptive statistics

Standard deviation	2361.800637
Coefficient of variation (CV)	0.596845029
Kurtosis	-1.222934817
Mean	3957.142177
Median Absolute Deviation (MAD)	2038
Skewness	0.05171443885
Sum	26580124
Variance	5578102.25
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	1	< 0.1%
5381	1	< 0.1%
5353	1	< 0.1%
5352	1	< 0.1%
5350	1	< 0.1%
5349	1	< 0.1%
5348	1	< 0.1%
5347	1	< 0.1%
5346	1	< 0.1%
5345	1	< 0.1%
Other values (6707)	6707	99.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1	< 0.1%
1	1	< 0.1%
2	1	< 0.1%
3	1	< 0.1%
4	1	< 0.1%
5	1	< 0.1%
6	1	< 0.1%
7	1	< 0.1%
8	1	< 0.1%
9	1	< 0.1%

Value	Count	Frequency (%)
8125	1	< 0.1%
8124	1	< 0.1%
8123	1	< 0.1%
8122	1	< 0.1%
8121	1	< 0.1%
8120	1	< 0.1%
8119	1	< 0.1%
8118	1	< 0.1%
8116	1	< 0.1%
8115	1	< 0.1%

name
Categorical

HIGH CARDINALITY

Distinct	1982
Distinct (%)	29.5%
Missing	0
Missing (%)	0.0%
Memory size	52.6 KiB

Maruti Swift Dzire VDI	118
Maruti Alto 800 LXI	76
Maruti Alto LXi	69
Maruti Swift VDI	60
Maruti Alto K10 VXI	47
Other values (1977)	6347

Length

Max length	54
Median length	24
Mean length	25.19994045
Min length	11

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	898 ?
Unique (%)	13.4%

Sample

1st row	Maruti Swift Dzire VDI
2nd row	Skoda Rapid 1.5 TDI Ambition
3rd row	Honda City 2017-2020 EXi
4th row	Hyundai i20 Sportz Diesel
5th row	Maruti Swift VXI BSIII

Common Values

Value	Count	Frequency (%)
Maruti Swift Dzire VDI	118	1.8%
Maruti Alto 800 LXI	76	1.1%
Maruti Alto LXi	69	1.0%
Maruti Swift VDI	60	0.9%
Maruti Alto K10 VXI	47	0.7%
Hyundai EON Era Plus	44	0.7%
Maruti Wagon R VXI BS IV	43	0.6%
Maruti Alto LX	43	0.6%
Maruti Ertiga VDI	42	0.6%
Maruti Ritz VDi	40	0.6%
Other values (1972)	6135	91.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
maruti	2089	6.6%
hyundai	1214	3.8%
mahindra	709	2.2%
tata	633	2.0%
swift	620	2.0%
diesel	545	1.7%
bsiv	542	1.7%
1.2	502	1.6%
vxi	476	1.5%
plus	475	1.5%
Other values (825)	23905	75.4%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

year
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	27
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2013.611136

Minimum	1994
Maximum	2020
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	52.6 KiB

Quantile statistics

Minimum	1994
5-th percentile	2007
Q1	2011
median	2014
Q3	2017
95-th percentile	2019
Maximum	2020
Range	26
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	3.897401569
Coefficient of variation (CV)	0.001935528414
Kurtosis	1.166627829
Mean	2013.611136
Median Absolute Deviation (MAD)	3
Skewness	-0.931471173
Sum	13525426
Variance	15.18973899
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=27)

Value	Count	Frequency (%)
2017	802	11.9%
2016	691	10.3%
2015	680	10.1%
2018	607	9.0%
2014	580	8.6%
2012	576	8.6%
2013	560	8.3%
2011	535	8.0%
2010	361	5.4%
2019	347	5.2%
Other values (17)	978	14.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1994	2	< 0.1%
1995	1	< 0.1%
1996	2	< 0.1%
1997	9	0.1%
1998	9	0.1%
1999	13	0.2%
2000	14	0.2%
2001	6	0.1%
2002	19	0.3%
2003	36	0.5%

Value	Count	Frequency (%)
2020	63	0.9%
2019	347	5.2%
2018	607	9.0%
2017	802	11.9%
2016	691	10.3%
2015	680	10.1%
2014	580	8.6%
2013	560	8.3%
2012	576	8.6%
2011	535	8.0%

selling_price
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	670
Distinct (%)	10.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	526385.997

Minimum	29999
Maximum	10000000
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	52.6 KiB

Quantile statistics

Minimum	29999
5-th percentile	110000
Q1	250000
median	420000
Q3	650000
95-th percentile	1200000
Maximum	10000000
Range	9970001
Interquartile range (IQR)	400000

Descriptive statistics

Standard deviation	523550.4483
Coefficient of variation (CV)	0.994613176
Kurtosis	52.48996792
Mean	526385.997
Median Absolute Deviation (MAD)	185000
Skewness	5.57076348
Sum	3535734742
Variance	2.741050719 × 10¹¹
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
300000	208	3.1%
350000	196	2.9%
600000	167	2.5%
400000	164	2.4%
250000	161	2.4%
550000	160	2.4%
500000	160	2.4%
450000	147	2.2%
650000	145	2.2%
200000	134	2.0%
Other values (660)	5075	75.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
29999	1	< 0.1%
30000	1	< 0.1%
31000	1	< 0.1%
31504	1	< 0.1%
33351	1	< 0.1%
35000	3	< 0.1%
39000	1	< 0.1%
40000	11	0.2%
42000	2	< 0.1%
45000	21	0.3%

Value	Count	Frequency (%)
10000000	1	< 0.1%
7200000	1	< 0.1%
6523000	1	< 0.1%
6223000	1	< 0.1%
6000000	3	< 0.1%
5923000	1	< 0.1%
5850000	1	< 0.1%
5830000	1	< 0.1%
5800000	2	< 0.1%
5500000	4	0.1%

km_driven
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	898
Distinct (%)	13.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	73398.33765

Minimum	1
Maximum	2360457
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	52.6 KiB

Quantile statistics

Minimum	1
5-th percentile	11500
Q1	38000
median	68203
Q3	100000
95-th percentile	155000
Maximum	2360457
Range	2360456
Interquartile range (IQR)	62000

Descriptive statistics

Standard deviation	58703.27527
Coefficient of variation (CV)	0.7997902561
Kurtosis	397.3335813
Mean	73398.33765
Median Absolute Deviation (MAD)	31797
Skewness	11.91618609
Sum	493016634
Variance	3446074527
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
120000	487	7.3%
70000	420	6.3%
80000	405	6.0%
60000	383	5.7%
50000	346	5.2%
100000	305	4.5%
90000	301	4.5%
40000	280	4.2%
110000	256	3.8%
30000	213	3.2%
Other values (888)	3321	49.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	1	< 0.1%
1000	5	0.1%
1300	1	< 0.1%
1303	1	< 0.1%
1500	2	< 0.1%
1600	1	< 0.1%
1620	1	< 0.1%
2000	7	0.1%
2118	1	< 0.1%
2136	1	< 0.1%

Value	Count	Frequency (%)
2360457	1	< 0.1%
1500000	1	< 0.1%
577414	1	< 0.1%
500000	2	< 0.1%
475000	1	< 0.1%
440000	1	< 0.1%
426000	1	< 0.1%
380000	1	< 0.1%
376412	1	< 0.1%
375000	1	< 0.1%

fuel
Categorical

HIGH CORRELATION

Distinct	4
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	52.6 KiB

Diesel	3658
Petrol	2973
CNG	51
LPG	35

Length

Max length	6
Median length	6
Mean length	5.961589996
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Diesel
2nd row	Diesel
3rd row	Petrol
4th row	Diesel
5th row	Petrol

Common Values

Value	Count	Frequency (%)
Diesel	3658	54.5%
Petrol	2973	44.3%
CNG	51	0.8%
LPG	35	0.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
diesel	3658	54.5%
petrol	2973	44.3%
cng	51	0.8%
lpg	35	0.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

seller_type
Categorical

Distinct	3
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	52.6 KiB

Individual	6024
Dealer	666
Trustmark Dealer	27

Length

Max length	16
Median length	10
Mean length	9.627512282
Min length	6

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Individual
2nd row	Individual
3rd row	Individual
4th row	Individual
5th row	Individual

Common Values

Value	Count	Frequency (%)
Individual	6024	89.7%
Dealer	666	9.9%
Trustmark Dealer	27	0.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
individual	6024	89.3%
dealer	693	10.3%
trustmark	27	0.4%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

transmission
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	52.6 KiB

Manual	6142
Automatic	575

Length

Max length	9
Median length	6
Mean length	6.256811076
Min length	6

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Manual
2nd row	Manual
3rd row	Manual
4th row	Manual
5th row	Manual

Common Values

Value	Count	Frequency (%)
Manual	6142	91.4%
Automatic	575	8.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
manual	6142	91.4%
automatic	575	8.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

owner
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	52.6 KiB

First Owner	4176
Second Owner	1888
Third Owner	493
Fourth & Above Owner	155
Test Drive Car	5

Length

Max length	20
Median length	11
Mean length	11.490993
Min length	11

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	First Owner
2nd row	Second Owner
3rd row	Third Owner
4th row	First Owner
5th row	First Owner

Common Values

Value	Count	Frequency (%)
First Owner	4176	62.2%
Second Owner	1888	28.1%
Third Owner	493	7.3%
Fourth & Above Owner	155	2.3%
Test Drive Car	5	0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
owner	6712	48.8%
first	4176	30.4%
second	1888	13.7%
third	493	3.6%
fourth	155	1.1%
	155	1.1%
above	155	1.1%
test	5	< 0.1%
drive	5	< 0.1%
car	5	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

seats
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	9
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5.434271252

Minimum	2
Maximum	14
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	52.6 KiB

Quantile statistics

Minimum	2
5-th percentile	5
Q1	5
median	5
Q3	5
95-th percentile	7
Maximum	14
Range	12
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.9838050408
Coefficient of variation (CV)	0.1810371612
Kurtosis	3.608223114
Mean	5.434271252
Median Absolute Deviation (MAD)	0
Skewness	1.919315131
Sum	36502
Variance	0.9678723582
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=9)

Value	Count	Frequency (%)
5	5254	78.2%
7	966	14.4%
8	221	3.3%
4	124	1.8%
9	74	1.1%
6	57	0.8%
10	18	0.3%
2	2	< 0.1%
14	1	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
2	2	< 0.1%
4	124	1.8%
5	5254	78.2%
6	57	0.8%
7	966	14.4%
8	221	3.3%
9	74	1.1%
10	18	0.3%
14	1	< 0.1%

Value	Count	Frequency (%)
14	1	< 0.1%
10	18	0.3%
9	74	1.1%
8	221	3.3%
7	966	14.4%
6	57	0.8%
5	5254	78.2%
4	124	1.8%
2	2	< 0.1%

mileage KMPL
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	376
Distinct (%)	5.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	19.49667832

Minimum	9
Maximum	30.46
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	52.6 KiB

Quantile statistics

Minimum	9
5-th percentile	12.9
Q1	16.8
median	19.46658478
Q3	22.5
95-th percentile	25.83
Maximum	30.46
Range	21.46
Interquartile range (IQR)	5.7

Descriptive statistics

Standard deviation	3.915238225
Coefficient of variation (CV)	0.2008156549
Kurtosis	-0.510254775
Mean	19.49667832
Median Absolute Deviation (MAD)	2.853415215
Skewness	0.007906554036
Sum	130959.1883
Variance	15.32909036
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
18.9	210	3.1%
19.7	168	2.5%
18.6	150	2.2%
21.1	147	2.2%
17	124	1.8%
15.96	108	1.6%
16.1	106	1.6%
17.8	96	1.4%
12.8	88	1.3%
15.1	86	1.3%
Other values (366)	5434	80.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
9	4	0.1%
9.5	1	< 0.1%
10	2	< 0.1%
10.1	2	< 0.1%
10.5	17	0.3%
10.71	1	< 0.1%
10.75	2	< 0.1%
10.8	1	< 0.1%
10.9	3	< 0.1%
10.91	4	0.1%

Value	Count	Frequency (%)
30.46	2	< 0.1%
28.4	85	1.3%
28.09	31	0.5%
27.62	6	0.1%
27.4	4	0.1%
27.39	24	0.4%
27.3	10	0.1%
27.28	13	0.2%
26.83	2	< 0.1%
26.8	3	< 0.1%

engine CC
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	121
Distinct (%)	1.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1433.989377

Minimum	793
Maximum	3604
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	52.6 KiB

Quantile statistics

Minimum	793
5-th percentile	796
Q1	1197
median	1248
Q3	1498
95-th percentile	2499
Maximum	3604
Range	2811
Interquartile range (IQR)	301

Descriptive statistics

Standard deviation	490.9976244
Coefficient of variation (CV)	0.342399764
Kurtosis	0.9926247184
Mean	1433.989377
Median Absolute Deviation (MAD)	245
Skewness	1.232357125
Sum	9632106.646
Variance	241078.6671
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1248	907	13.5%
1197	698	10.4%
796	420	6.3%
998	398	5.9%
2179	339	5.0%
1498	336	5.0%
1396	281	4.2%
1199	192	2.9%
2523	183	2.7%
1461	169	2.5%
Other values (111)	2794	41.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
793	6	0.1%
796	420	6.3%
799	71	1.1%
814	111	1.7%
909	2	< 0.1%
936	34	0.5%
993	26	0.4%
995	42	0.6%
998	398	5.9%
999	69	1.0%

Value	Count	Frequency (%)
3604	1	< 0.1%
3498	1	< 0.1%
3198	4	0.1%
2999	2	< 0.1%
2997	2	< 0.1%
2993	13	0.2%
2987	8	0.1%
2982	27	0.4%
2967	8	0.1%
2956	19	0.3%

power BHP
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	318
Distinct (%)	4.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	87.76610019

Minimum	32.8
Maximum	400
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	52.6 KiB

Quantile statistics

Minimum	32.8
5-th percentile	47.3
Q1	67.1
median	81.83
Q3	100
95-th percentile	147.9
Maximum	400
Range	367.2
Interquartile range (IQR)	32.9

Descriptive statistics

Standard deviation	31.72455521
Coefficient of variation (CV)	0.3614670714
Kurtosis	5.426867236
Mean	87.76610019
Median Absolute Deviation (MAD)	14.79
Skewness	1.711327357
Sum	589524.895
Variance	1006.447403
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
74	324	4.8%
88.5	193	2.9%
46.3	158	2.4%
67	152	2.3%
67.1	141	2.1%
81.8	136	2.0%
67.04	136	2.0%
70	134	2.0%
47.3	131	2.0%
62.1	130	1.9%
Other values (308)	5082	75.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
32.8	2	< 0.1%
34.2	20	0.3%
35	19	0.3%
35.5	2	< 0.1%
37	88	1.3%
37.48	11	0.2%
37.5	6	0.1%
38	2	< 0.1%
38.4	2	< 0.1%
40.3	2	< 0.1%

Value	Count	Frequency (%)
400	1	< 0.1%
282	1	< 0.1%
280	1	< 0.1%
272	1	< 0.1%
270.9	3	< 0.1%
265	1	< 0.1%
261.4	4	0.1%
258	2	< 0.1%
254.8	3	< 0.1%
254.79	1	< 0.1%

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	df_index	name	year	selling_price	km_driven	fuel	seller_type	transmission	owner	seats	mileage KMPL	engine CC	power BHP
0	0	Maruti Swift Dzire VDI	2014	450000	145500	Diesel	Individual	Manual	First Owner	5.00	23.40	1248.00	74.00
1	1	Skoda Rapid 1.5 TDI Ambition	2014	370000	120000	Diesel	Individual	Manual	Second Owner	5.00	21.14	1498.00	103.52
2	2	Honda City 2017-2020 EXi	2006	158000	140000	Petrol	Individual	Manual	Third Owner	5.00	17.70	1497.00	78.00
3	3	Hyundai i20 Sportz Diesel	2010	225000	127000	Diesel	Individual	Manual	First Owner	5.00	23.00	1396.00	90.00
4	4	Maruti Swift VXI BSIII	2007	130000	120000	Petrol	Individual	Manual	First Owner	5.00	16.10	1298.00	88.20
5	5	Hyundai Xcent 1.2 VTVT E Plus	2017	440000	45000	Petrol	Individual	Manual	First Owner	5.00	20.14	1197.00	81.86
6	6	Maruti Wagon R LXI DUO BSIII	2007	96000	175000	LPG	Individual	Manual	First Owner	5.00	17.30	1061.00	57.50
7	7	Maruti 800 DX BSII	2001	45000	5000	Petrol	Individual	Manual	Second Owner	4.00	16.10	796.00	37.00
8	8	Toyota Etios VXD	2011	350000	90000	Diesel	Individual	Manual	First Owner	5.00	23.59	1364.00	67.10
9	9	Ford Figo Diesel Celebration Edition	2013	200000	169000	Diesel	Individual	Manual	First Owner	5.00	20.00	1399.00	68.10

Last rows

	df_index	name	year	selling_price	km_driven	fuel	seller_type	transmission	owner	seats	mileage KMPL	engine CC	power BHP
6707	8115	Maruti 800 AC	1997	40000	120000	Petrol	Individual	Manual	First Owner	4.00	16.10	796.00	37.00
6708	8116	Maruti Alto K10 VXI Airbag	2017	340000	45000	Petrol	Individual	Manual	First Owner	5.00	23.95	998.00	67.10
6709	8118	Hyundai i20 Magna	2013	380000	25000	Petrol	Individual	Manual	First Owner	5.00	18.50	1197.00	82.85
6710	8119	Maruti Wagon R LXI Optional	2017	360000	80000	Petrol	Individual	Manual	First Owner	5.00	20.51	998.00	67.04
6711	8120	Hyundai Santro Xing GLS	2008	120000	191000	Petrol	Individual	Manual	First Owner	5.00	17.92	1086.00	62.10
6712	8121	Maruti Wagon R VXI BS IV with ABS	2013	260000	50000	Petrol	Individual	Manual	Second Owner	5.00	18.90	998.00	67.10
6713	8122	Hyundai i20 Magna 1.4 CRDi	2014	475000	80000	Diesel	Individual	Manual	Second Owner	5.00	22.54	1396.00	88.73
6714	8123	Hyundai i20 Magna	2013	320000	110000	Petrol	Individual	Manual	First Owner	5.00	18.50	1197.00	82.85
6715	8124	Hyundai Verna CRDi SX	2007	135000	119000	Diesel	Individual	Manual	Fourth & Above Owner	5.00	16.80	1493.00	110.00
6716	8125	Maruti Swift Dzire ZDi	2009	382000	120000	Diesel	Individual	Manual	First Owner	5.00	19.30	1248.00	73.90

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Interactions

Correlations

Spearman's ρ

Pearson's r

Kendall's τ

Cramér's V (φc)

Phik (φk)

Missing values

Sample

First rows

Last rows